R/runread_biomee_f.R

In rsofun: The P-Model and BiomeE Modelling Framework

#' Run BiomeE
#'
#' Runs BiomeE model for multiple sites.
#'
#' @param drivers A nested data frame with one row for each site and columns
#'  named according to the arguments of function `runread_biomee_f_bysite()`.
#'  See `?run_biomee_f_bysite` for the list of parameters and forcing data required.
#' @param makecheck Flag specifying whether checks are performed to verify forcings.
#' @param parallel Flag specifying whether simulations are to be
#'  parallelised (sending data from a certain number of sites to each core). 
#'  Defaults to \code{FALSE}.
#' @param ncores An integer specifying the number of cores used for parallel 
#' computing. Defaults to 2.
#'
#' @return A tibble with one row for each site and model outputs stored 
#' in the nested column \code{data}. See `?run_biomee_f_bysite` for a
#' description of the BiomeE output variables.
#' @export
#' 
#' @examples 
#' \donttest{
#' # Example BiomeE model run
#' 
#' runread_biomee_f(
#'   drivers = biomee_gs_leuning_drivers
#' )
#' runread_biomee_f(
#'   drivers = biomee_p_model_drivers
#' )
#' }

runread_biomee_f <- function(
    drivers,
    makecheck = TRUE,
    parallel = FALSE,
    ncores = 2
){
  
  # predefine variables for CRAN check compliance
  forcing <- init_cohort <- init_soil <- data <-
    input <- params_siml <- params_species <-
    params_tile <- site_info <- sitename <- . <- NULL
  
  if (parallel){
    
    cl <- multidplyr::new_cluster(ncores) %>% 
      multidplyr::cluster_assign(makecheck = FALSE) %>% 
      multidplyr::cluster_library(
        packages = c("dplyr", "purrr", "rsofun")
      )
    
    ## distribute to to cores, making sure all data from a specific site is sent to the same core
    df_out <- drivers %>%
      dplyr::group_by( id = row_number() ) %>%
      tidyr::nest('input' = c("sitename",
                              "params_siml",
                              "site_info",
                              "forcing",
                              "params_tile",
                              "params_species",
                              "init_cohort",
                              "init_soil")) %>%
      multidplyr::partition(cl) %>%
      dplyr::mutate('data' = purrr::map(input,
                                        ~run_biomee_f_bysite(
                                          sitename       = .x$sitename[[1]], 
                                          params_siml    = .x$params_siml[[1]], 
                                          site_info      = .x$site_info[[1]], 
                                          forcing        = .x$forcing[[1]], 
                                          params_tile    = .x$params_tile[[1]], 
                                          params_species = .x$params_species[[1]],
                                          init_cohort    = .x$init_cohort[[1]], 
                                          init_soil      = .x$init_soil[[1]], 
                                          makecheck      = makecheck )
                                        
      )) %>% 
      dplyr::collect() %>%
      dplyr::ungroup() %>%
      dplyr::select('data')  %>% 
      tidyr::unnest(cols = c('data'))
    
  } else {
    
    df_out <- drivers %>% 
      dplyr::select("sitename", 
                    "params_siml", 
                    "site_info", 
                    "forcing", 
                    "params_tile", 
                    "params_species",
                    "init_cohort", 
                    "init_soil"
      ) %>% 
      dplyr::mutate(data = purrr::pmap(
        .,
        run_biomee_f_bysite,
        makecheck = makecheck
      )) %>% 
      dplyr::select(sitename, data) 
    
  }
  
  return(df_out)
}